New data are incorporated in the
network without retraining its weights => fast and accurate
extrapolation;

The method is based on a
significant neurophysiological background.

ASNN represents a
combination of an ensemble of feed-forward neural networks and the
k-nearest neighbour technique. This method uses the correlation between
ensemble responses as a measure of distance amid the analysed cases for
the nearest neighbour technique. This provides an improved prediction by
the bias correction of the neural network ensemble. An associative
neural network has a memory that can coincide with the training set. If
new data becomes available, the network further improves its predictive
ability and provides a reasonable approximation of the unknown function
without a need to retrain the neural network ensemble. This feature of
the method dramatically improves its predictive ability over traditional
neural networks and k-nearest neighbour techniques. Another important
feature of ASNN is the possibility to interpret neural network results
by analysis of correlations between data cases in the space of models.

A standalone version of our software is also available.The data input format is described here.